earnings call transcript speaker labels wrong after parse error

Earnings call transcript speaker labels are wrong after parsing

TL;DR

Speaker labels go wrong in parsing because transcripts mark speakers inconsistently, with name variations, titles, and analyst-firm combos that a naive split on colons mangles. Parse speakers with patterns anchored to turn boundaries, then normalize each label against a roster of expected speakers for the call. Validate by checking that the CEO and CFO labels actually utter the prepared remarks.

The error

(wrong labels, no exception)
speaker labels wrong after parse: analyst names merged, "Operator" turns attributed to CEO, Q and A misaligned

When this helps

• transcript speaker labels are wrong or merged after parsing
• quotes in a briefing are attributed to the wrong person
• building speaker-indexed Q and A
• validating a new transcript parser

When it doesn't

• the transcript has no speaker labels at all; some providers omit them
• you need voice-based diarization; text parsing cannot do that
• labels are right but timestamps are broken; that is a separate fix

Works with

python 3.8+ with re. Label styles vary by transcript provider.

Steps

1. Inspect the raw speaker marking style

grep -m 10 -E "^[A-Z][a-z]+ [A-Z]" transcript_raw.txt | head -10

Expected: The actual label style. Note variations: full names, first names, titles, and firm names in parentheses.

2. Split turns on speaker boundaries, not bare colons

import re
raw = open("transcript_raw.txt").read()
turns = re.split(r"\n(?=[A-Z][a-z]+ [A-Z][a-z]+:)", raw)
print("turns found:", len(turns))
print("first label:", turns[1].split(":")[0] if len(turns) != 1 else "none")

Expected: A turn count near the expected speaker-turn count. Anchoring on name-colon at line starts avoids splitting on colons inside speech.

3. Normalize labels against the call roster

import re
roster = {"Jane Smith": "CEO", "John Doe": "CFO", "Operator": "Operator"}
raw = open("transcript_raw.txt").read()
labels = re.findall(r"\n([A-Z][a-z]+ [A-Z][a-z]+|Operator):", raw)
known = [l for l in labels if l in roster or " " in l]
print("labels:", len(labels), "recognizable:", len(known))

Expected: Most labels mapping to known speakers. Unmatched labels are usually analysts; keep their names and tag the firm separately.

4. Validate that prepared remarks carry executive labels

import re
raw = open("transcript_raw.txt").read()
turns = re.split(r"\n(?=[A-Z][a-z]+ [A-Z][a-z]+:)", raw)
remarks = [t for t in turns if "good morning" in t.lower()][:2]
for r in remarks:
    print("remarks label:", r.split(":")[0][:40])

Expected: Executive names on the opening remarks. If an analyst label appears there, the turn splitting is off and needs repair before the briefing uses quotes.

Other ways people phrase this

transcript speaker diarization labels wrong

Text-level label parsing, not audio diarization. The fix is pattern plus roster, not a new model.

earnings call operator turns misattributed

Operator lines have a distinct style. Handle them as a special case before the general split.

analyst names merged transcript parse

Analyst plus firm on one line defeats naive splits. Split the person from the firm after turn detection.

Why it happens

Transcript providers format speaker labels with no standard: names, titles, firms, and inconsistent punctuation. Parsers that split on any colon shred sentences containing colons and merge multi-line labels. Labels are metadata about turns, so parsing must be turn-aware, not line-aware.

Edge cases

• Some providers label every analyst turn with firm name; others only the first. Normalize per provider.
• Overlapping speech gets a single label; do not invent a second speaker.
• Translated transcripts may reorder name and title; validate the roster per language.
• Corrections issued by the provider change labels; re-parse when a corrected transcript appears.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_mx1iH6mw85XtwUZl9QRGuQ